000			01513nam a2200205Ia 4500
008			210916s9999 xx 000 0 und d
020			_a9780387310732
082			_a006.4 _bBIS
100			_aBishop, Christopher M. _9548
245		0	_aPattern recognition and machine learning
260			_aNew York _bSpringer _c2006
300			_axx, 738p.
520			_aPattern recognition has its origins in engineering, whereas machine learning grew out of computer science. However, these activities can be viewed as two facets of the same field, and together they have undergone substantial development over the past ten years. In particular, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic models. Also, the practical applicability of Bayesian methods has been greatly enhanced through the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation. Similarly, new models based on kernels have had a significant impact on both algorithms and applications. This new textbook reflects these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning.
650			_aMachine learning _9481
650			_aPattern perception _9563
650			_aPattern recognition systems _9553
650			_aMathematical statistics _9638
650			_aArtificial intelligence _9560
942			_cBK
999			_c6910 _d6910